Raw behavioral signals are noisy and incomplete. Sleep quality tells one story. Movement patterns tell another. Meal timing, device interaction rhythms, environmental context—each stream provides partial information.

The signal fusion engine synthesizes these multi-modal inputs into a unified behavioral state estimation. Not just "you slept 7 hours" but "your sleep architecture was disrupted, movement was low, and you're likely to have an energy dip around 2pm."

Individual signals are data. Fused signals are understanding.

The system operates at three temporal scales: micro (hourly aggregation), meso (daily/weekly patterns), and macro (monthly trends). Each timescale reveals different insights. Hourly data shows decision points. Weekly data shows behavioral physics. Monthly data shows capability growth.

Behavioral change happens at multiple frequencies simultaneously. A single coaching loop can't capture all three. The system implements nested loops, each with distinct purposes:

The micro loop closes in real-time: intervention → behavior → outcome → adjustment. The meso loop closes over weeks: patterns emerge, recommendations personalize, confidence grows. The macro loop closes over months: user capability increases, system intervention decreases.

Each loop frequency feeds into the next. Real-time decisions inform weekly patterns. Weekly patterns build monthly capability.

The interface isn't static—it adapts to user capability. Early users need dense explanations, active coaching, mental model building. Experienced users need brief confirmations and subtle nudges. Autonomous users need ambient presence at most.

Week 1: "Your sleep last night was fragmented. Here's why that matters for today's energy. Consider a lighter workout and earlier dinner to recover."

Week 6: "Sleep fragmented. Light day recommended."

Week 12: Subtle indicator that system detected disruption. No text needed—user already knows what to do.

Success means you don't need the app anymore. The goal is graduation, not retention.

This inverts the typical app business model. Most apps optimize for engagement and time-in-app. This system optimizes for capability transfer and autonomy building. The measure of success isn't daily active users—it's how quickly users can self-direct without prompts.

Every feature is defined by what it deliberately excludes:

The architecture also extends beyond wellness. The same three-loop structure with tighter tolerances and medical-grade sensing works for chronic condition management. Diabetes, hypertension, cardiac rehab—all benefit from the same pattern: signal fusion, nested intervention loops, adaptive density, autonomy building.

The framework scales from wellness tracking to clinical-grade health management.